In this work, we proposed a novel contour-driven approach for detecting cluttered and occluded contraband items (and their instances) within the baggage X-ray scans, hypothesizing that contours are the most robust cues given the lack of texture in the X-ray imagery. We concretized this original approach through a tensor pooling module, producing multi-scale tensor maps highlighting the items' outlines within the X-ray scans and an instance segmentation model acting on this representation.
We validated our approach on three publicly available datasets encompassing gray-level and colored scans and showcased its overall superiority over competitive frameworks in various aspects. For instance, the proposed framework outperforms the state-of-the-art methods [1]}, [2]}, [3]}, [4]} by 1.07%, 0.190%, and 1.55% on GDXray [5]}, SIXray [6]}, and OPIXray [1]} dataset, respectively. Furthermore, on each SIXray subsets (i.e., SIXray10, SIXray100, SIXray1000) [6]}, the proposed framework leads the state-of-the-art by 1.92%, 2.02%, and 3.22%, respectively.
